matplotlib-users Mailing List for matplotlib

Incidentally, if anyone else wants to do this and is unable to update their matplotlib to v1.2.0, this code snippet achieves the same effect.
#######################################
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
# the line to plot
x = np.linspace(0,1,101)
y = x*0.5-0.25
scaling = np.exp(-(x-0.5)**2/0.5**2) # scale the transparency of the line according to this
points = np.array([x, y]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
smin = scaling.min()
smax = scaling.max()
# inline function to convert scaling value to alpha value in [0,1]
alpha = lambda s0: (s0-smin)/(smax-smin)
 
# create a (r,g,b,a) color description for each line segment
cmap = []
for a in segments:
 # the x-value for this segment is:
 x0 = a.mean(0)[0]
 # so it has a scaling value of:
 s0 = np.interp(x0,x,scaling)
 # and it has an alpha value of:
 a0 = alpha(s0)
 cmap.append([0.0,0.0,0.0,a0])
 
# Create the line collection object, and set the color parameters.
lc = LineCollection(segments)
lc.set_color(cmap)
ax = plt.subplot(111)
ax.add_collection(lc)
ax.set_xlim(x.min(), x.max())
ax.set_ylim(y.min(), y.max())
plt.show()
#######################################

On 12/5/12 8:36 PM, Dale Lukas Peterson wrote:
> I have a time series of 32-bit unsigned integers in the form of a
> numpy array with dtype=numpy.uint32. The bits of each integer in
> array correspond to various boolean states collected during an
> experiment. I would like to make a plot this data in the following
> way:
> 1) time on the horizontal axis (I have a time array of the same
> length as my integer array)
> 2) bit position N on the vertical axis (0-1 corresponds to bit 0, 1-2
> corresponds to bit 1, etc.)
> 3) a solid filled rectangle from (t1, N) to (t1+dt, N+1) whenever the
> bit is high.
>
> I been able to use the bitwise_and() and right_shift() on the data to
> get individual arrays which are populated with only 0's and1's.
>
> What I'm not clear on is now how to get the solid filled rectangle for
> areas where the bit is high and nothing when the bit is low.
>
> Is there already this functionality somewhere in matplotlib? I looked
> in the gallery and couldn't find anything quite like what I'm looking
> for, though I may have simply missed it. If there isn't, I'm sure
> there is a way to do it, does anybody have any recommendations as to
> the path of least resistance?
Just FYI, this reminds me a lot of the recent EventRaster discussion:
http://thread.gmane.org/gmane.comp.python.matplotlib.devel/11458/focus=11655
Thanks,
Jason

Hi Luke,
> I been able to use the bitwise_and() and right_shift() on the data to
> get individual arrays which are populated with only 0's and1's.
> 
> What I'm not clear on is now how to get the solid filled rectangle for
> areas where the bit is high and nothing when the bit is low.
> 
> Is there already this functionality somewhere in matplotlib? I looked
> in the gallery and couldn't find anything quite like what I'm looking
> for, though I may have simply missed it. If there isn't, I'm sure
> there is a way to do it, does anybody have any recommendations as to
> the path of least resistance?
How about using imshow()? Turn your individual arrays into a three-dimensional array of (r,g,b,a) values (MxNx4 array). You can then overwrite the axis labels (pixel numbers) with the boolean state descriptors and the time stamps. 
Cheers,
Oliver

Dear Matplotlibers,
I'm trying to insert a logo image (from PNG) in the top left corner of my axes. I'm doing this by reading the logo using read_png() and inserting it as an OffsetImage in an AnnotationBox. When saving the figure as PNG everything is based on pixel sizes and I know how to scale the OffsetImage such that I end up with the desired logo size (based on the figure and logo sizes in pixels). Saving as PDF, however, does not depend on pixel sizes, and I end up with a 'different' size of the logo.
Could someone please tell me what determines the size of the OffsetImage when saving as PDF? And maybe even better, how to achieve scaling that results in the same size logo as in the PNG output?
I have attached:
1a) A small test.py script that produces PNG and PDF output showing the different logo sizes. Uncommenting the zoom_factor line shows how to scale the logo independent of the figure size/dpi settings.
1b) A dummy PNG logo file.
2) Two example output files (PNG and PDF) showing the output of test.py.
Any hints would be much appreciated!
Best regards,
Jeroen

I have a time series of 32-bit unsigned integers in the form of a
numpy array with dtype=numpy.uint32. The bits of each integer in
array correspond to various boolean states collected during an
experiment. I would like to make a plot this data in the following
way:
1) time on the horizontal axis (I have a time array of the same
length as my integer array)
2) bit position N on the vertical axis (0-1 corresponds to bit 0, 1-2
corresponds to bit 1, etc.)
3) a solid filled rectangle from (t1, N) to (t1+dt, N+1) whenever the
bit is high.
I been able to use the bitwise_and() and right_shift() on the data to
get individual arrays which are populated with only 0's and1's.
What I'm not clear on is now how to get the solid filled rectangle for
areas where the bit is high and nothing when the bit is low.
Is there already this functionality somewhere in matplotlib? I looked
in the gallery and couldn't find anything quite like what I'm looking
for, though I may have simply missed it. If there isn't, I'm sure
there is a way to do it, does anybody have any recommendations as to
the path of least resistance?
Thanks,
Luke
-- 
"People call me a perfectionist, but I'm not. I'm a rightist. I do
something until it's right, and then I move on to the next thing."
― James Cameron